import os
from PIL import Image
import torch.utils.data as data
import torch
import torchvision.transforms as transforms

class SalObjDataset(data.Dataset):
    def __init__(self, image_root, gt_root, depth_root , boundary_root, trainsize):
        self.trainsize = trainsize
        self.images = [image_root + f for f in os.listdir(image_root) if f.endswith('.jpg')]
        self.gts = [gt_root + f for f in os.listdir(gt_root) if f.endswith('.jpg')
                    or f.endswith('.png')]
        self.bdrs = [boundary_root + f for f in os.listdir(boundary_root) if f.endswith('.jpg')
                    or f.endswith('.png')]
        self.depth = [depth_root + f for f in os.listdir(depth_root) if f.endswith('.jpg')
                    or f.endswith('.png')]
        self.depth = sorted(self.depth)
        self.images = sorted(self.images)
        self.gts = sorted(self.gts)
        self.bdrs = sorted(self.bdrs)
        self.filter_files()
        self.size = len(self.images)
        self.img_transform = transforms.Compose([
            transforms.Resize((self.trainsize, self.trainsize)),
            transforms.ToTensor(),
            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
        self.gt_transform = transforms.Compose([
            transforms.Resize((self.trainsize, self.trainsize)),
            transforms.ToTensor()])
        self.depth_transform = transforms.Compose([
            transforms.Resize((self.trainsize, self.trainsize)),
            transforms.ToTensor(),
         ])
        self.bdrs_transform = transforms.Compose([
            transforms.Resize((self.trainsize, self.trainsize)),
            transforms.ToTensor(),
        ])
    def __getitem__(self, index):
        image = self.rgb_loader(self.images[index])
        gt = self.binary_loader(self.gts[index])
        depth = self.binary_loader(self.depth[index])
        bdrs = self.binary_loader(self.bdrs[index])
        image = self.img_transform(image)
        gt = self.gt_transform(gt)
        bdrs = self.bdrs_transform(bdrs)
        depth = self.depth_transform(depth)
        #depth = torch.div(depth.float(),255.0)
        return image, gt, depth, bdrs
    def filter_files(self):
        assert len(self.images) == len(self.gts)
        images = []
        gts = []
        depth = []
        bdrs = []
        for img_path, gt_path, depth_path, bdr_path in zip(self.images, self.gts , self.depth, self.bdrs):
            img = Image.open(img_path)
            gt = Image.open(gt_path)
            depth_ = Image.open(depth_path)
            bdr_ = Image.open(bdr_path)
            if img.size == gt.size == depth_.size == bdr_.size:
                images.append(img_path)
                gts.append(gt_path)
                depth.append(depth_path)
                bdrs.append(bdr_path)
        self.images = images
        self.gts = gts
        self.depth = depth
        self.bdrs = bdrs
    def rgb_loader(self, path):
        with open(path, 'rb') as f:
            img = Image.open(f)
            return img.convert('RGB')
    def binary_loader(self, path):
        with open(path, 'rb') as f:
            img = Image.open(f)
            # return img.convert('1')
            return img.convert('L')
    def resize(self, img, gt):
        assert img.size == gt.size
        w, h = img.size
        if h < self.trainsize or w < self.trainsize:
            h = max(h, self.trainsize)
            w = max(w, self.trainsize)
            return img.resize((w, h), Image.BILINEAR), gt.resize((w, h), Image.NEAREST)
        else:
            return img, gt
    def __len__(self):
        return self.size
def get_loader(image_root, gt_root, depth_root ,boundary_root, batchsize, trainsize, shuffle=True, pin_memory=True):

    dataset = SalObjDataset(image_root, gt_root, depth_root,boundary_root,trainsize)
    data_loader = data.DataLoader(dataset=dataset,
                                  batch_size=batchsize,
                                  shuffle=shuffle,

                                  pin_memory=pin_memory)
    return data_loader

class test_dataset:
    def __init__(self, image_root, gt_root, depth_root, testsize):
        self.testsize = testsize
        self.images = [image_root + f for f in os.listdir(image_root) if f.endswith('.jpg')]
        self.gts = [gt_root + f for f in os.listdir(gt_root) if f.endswith('.jpg')
                       or f.endswith('.png')]
        self.depth = [depth_root + f for f in os.listdir(depth_root) if f.endswith('.jpg')
                      or f.endswith('.png')]
        self.images = sorted(self.images)
        self.gts = sorted(self.gts)
        self.depth = sorted(self.depth)
        self.transform = transforms.Compose([
            transforms.Resize((self.testsize, self.testsize)),
            transforms.ToTensor(),
            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
        self.gt_transform = transforms.ToTensor()
        self.depth_transform = transforms.Compose([
            transforms.Resize((self.testsize, self.testsize)),
            transforms.ToTensor(),
            transforms.Normalize([0.485,], [0.229,])])
        self.size = len(self.images)
        self.index = 0

    def load_data(self):
        image = self.rgb_loader(self.images[self.index])
        image = self.transform(image).unsqueeze(0)
        gt = self.binary_loader(self.gts[self.index])
        depth = self.binary_loader(self.depth[self.index])

        depth = self.depth_transform(depth).unsqueeze(0)
        #depth = torch.div(depth.float(), 255.0)
        name = self.images[self.index].split('\\')[-1]
        if name.endswith('.jpg'):
            name = name.split('.jpg')[0] + '.png'
        self.index += 1
        return image, gt,depth, name

    def rgb_loader(self, path):
        with open(path, 'rb') as f:
            img = Image.open(f)
            return img.convert('RGB')

    def binary_loader(self, path):
        with open(path, 'rb') as f:
            img = Image.open(f)
            return img.convert('L')


